Novel bioinformatic classification system for genetic signatures identification in diffuse large B-cell lymphoma

Abstract

Abstract Background Diffuse large B-cell lymphoma (DLBCL) is a spectrum of disease comprising more than 30% of non-Hodgkin lymphomas. Although studies have identified several molecular subgroups, the heterogeneous genetic background of DLBCL remains ambiguous. In this study we aimed to develop a novel approach and to provide a distinctive classification system to unravel its molecular features. Method A cohort of 342 patient samples diagnosed with DLBCL in our hospital were retrospectively enrolled in this study. A total of 46 genes were included in next-generation sequencing panel. Non-mutually exclusive genetic signatures for the factorization of complex genomic patterns were generated by random forest algorithm. Results A total of four non-mutually exclusive signatures were generated, including those with MYC-translocation (MYC-trans) (n = 62), with BCL2-translocation (BCL2-trans) (n = 69), with BCL6-translocation (BCL6-trans) (n = 108), and those with MYD88 and/or CD79B mutations (MC) signatures (n = 115). Comparison analysis between our model and traditional mutually exclusive Schmitz’s model demonstrated consistent classification pattern. And prognostic heterogeneity existed within EZB subgroup of de novo DLBCL patients. As for prognostic impact, MYC-trans signature was an independent unfavorable prognostic factor. Furthermore, tumors carrying three different signature markers exhibited significantly inferior prognoses compared with their counterparts with no genetic signature. Conclusion Compared with traditional mutually exclusive molecular sub-classification, non-mutually exclusive genetic fingerprint model generated from our study provided novel insight into not only the complex genetic features, but also the prognostic heterogeneity of DLBCL patients.

Keywords

• DLBCL
• Sequencing
• Random forest
• Classification
• Signature